LLM & GenAI Fundamentals is a core part of the Google Forward Deployed Engineer loop. Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. Below are the llm & genai fundamentals questions to prepare, the ones tagged to Google first, then the highest-signal questions from our LLM & GenAI Fundamentals track, each with an answer written to a senior-engineer bar.

WHAT GOOGLE LOOKS FOR HERE · Google Cloud significantly expanded its Forward Deployed Engineer program in 2026 to move enterprise customers from AI pilots to production; expect RAG, vector databases, and production GenAI deployment depth. See the full Google interview process →

LLM & GenAI Fundamentals questions tagged to Google

More LLM & GenAI Fundamentals questions for Google's loop

The highest-signal llm & genai fundamentals questions candidates rate most useful, modeled on what Google's Forward Deployed Engineer loop tests.

Concepts behind Google's LLM & GenAI Fundamentals round

The vocabulary and mental models these questions assume. Start with the foundations free; the deeper, interview-defining ideas are part of premium.

FoundationalTokenization & TokensA language model does not read characters or words. It reads tokens: sub-word chunks produced by a tokenizer, each mapped to an integer the model embeds. Tokens are the unit of the context window and of billing, and the way text splits into them explains a surprising number of model quirks, which is why almost every loop opens here.

FoundationalThe Context WindowThe context window is the fixed number of tokens a language model can attend to at once, and input and output share that same budget. Understanding it is what separates engineers who can size a prompt, control cost and latency, and decide when to reach for RAG from those who just paste everything in and hope.

FoundationalEmbeddings & Vector RepresentationsAn embedding turns a piece of text into a list of numbers positioned so that similar meanings land near each other in space, which lets you search by meaning instead of by keyword. Embeddings are the engine under RAG, semantic search, clustering, and deduplication, so FDE loops expect you to explain cosine similarity and the pitfalls that quietly break a vector index.

FoundationalThe Transformer, IntuitivelyThe transformer is the architecture behind every modern large language model, built on self-attention that lets each token look at every other token in parallel. FDE loops do not want the math; they want you to explain why attention beat RNNs, what decoder-only means, and why context length is expensive, in plain language an exec or a teammate can follow.

CoreSign inAttention and Self-AttentionAttention computes a weighted sum of value vectors, where the weights come from how well each token's query matches every other token's key. Self-attention applies this within one sequence so each token can pull from all the others, and the all-pairs comparison is why cost grows with the square of sequence length. FDE loops probe it because it explains context limits, latency, and the KV cache in one mental model.

CoreSign inRoPE and Positional EncodingsSelf-attention has no built-in sense of word order, so transformers inject position information into the token vectors. Rotary position embeddings (RoPE) rotate query and key vectors by a position-dependent angle so relative position falls out of the dot product, which is why RoPE underpins almost every long-context model and why extending a context window means rescaling RoPE.

FoundationalTemperature, Top-p and SamplingAt each step a model outputs a probability over every possible next token, and sampling settings like temperature, top-p, and top-k decide how that distribution is turned into an actual choice. FDE loops test this because it controls the determinism-versus-creativity dial: knowing when to set it low for extraction and high for brainstorming, and why the same prompt giving different answers is expected, not a bug.

Advanced🔒 PremiumLoRA and Parameter-Efficient Fine-tuningFull fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.

Google's Forward Deployed Engineer loop draws llm & genai fundamentals questions such as "Walk me through what actually happens when an LLM generates a response.", "Walk me through the transformer architecture end-to-end, minus the heavy math.", "What is a context window, and what are its practical limits in production?". Tokens, context windows, prompting vs RAG vs fine-tuning, hallucinations, evals, cost and latency: the conceptual core every AI-native FDE loop probes first. The full set, ordered easy to hard with expert answers, is below.

Work the llm & genai fundamentals questions on this page in order, study the underlying concepts linked below, then review Google's full interview process for how this round fits the loop. For Google specifically, interviewers weight this: Google Cloud significantly expanded its Forward Deployed Engineer program in 2026 to move enterprise customers from AI pilots to production; expect RAG, vector databases, and production GenAI deployment depth.

Yes, at scale. Google Cloud runs a large, multi-level Forward Deployed Engineer program, alongside Customer Engineers and Applied AI roles, embedding engineers with enterprise customers to ship agentic solutions on Vertex AI and Gemini Enterprise. It is one of the biggest FDE recruiters in 2026.

Expect practical coding, GenAI and agent system design, and role-related rounds on taking a Gemini or Vertex AI solution from proof of concept to production, plus customer-facing judgment. Interviewers probe how you scope ambiguous enterprise problems and build reliably.

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The other tracks Google's Forward Deployed Engineer loop tests.

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